Problem Statement

Traditional CNC fixtures are typically designed using conventional methods that prioritize rigidity but often result in overly bulky, material-intensive, and non-adaptive structures. These fixtures increase manufacturing costs, consume excessive material, and add unnecessary weight, which negatively affects machine performance due to higher inertia. Moreover, such fixtures lack flexibility and are not easily reconfigurable for different workpiece geometries—an essential requirement in today’s high-mix, low-volume manufacturing environments.

As machining speeds, accuracy requirements, and production variability increase, industries need fixtures that offer high stiffness-to-weight ratios, minimal deformation under cutting forces, and lower material usage—all while maintaining manufacturability. Current manual or experience-based design approaches cannot efficiently achieve these performance targets.

Therefore, there is a critical need for a fixture design approach that:

• Reduces weight without compromising structural stiffness

• Automatically adapts to different geometries

• Uses AI and optimization algorithms to improve performance

• Predicts stress and deformation without repeated simulations

• Meets Industry 4.0 expectations of intelligent, data-driven design systems

This project aims to address these gaps by developing an AI-assisted generative and topology-optimized CNC fixture, validated through FEA and ANN models, to achieve a lightweight, high-performance, and future-ready fixture system.